Messenger taps social nets

By
Kimberly Patch,
Technology Research NewsIf your friends and colleagues don't know
the answer to a given question, they often know of a better person to
ask. Several teams of researchers are looking to the fast, easy communication
of the Internet in order to leverage these social networks.

Researchers from the University of Michigan have brought the possibilities
a step forward with their Small-World Instant Messaging System (SWIM),
which extends instant messaging systems by identifying expertise and routing
queries accordingly.

The Small-World Instant Messaging System aims to efficiently tap
this expanded network of friends-of-friends, said Jun Zhang, a researcher
at the University of Michigan. In trying to use instant messaging and
email to get answers from friends and colleagues, Zhang noticed that many
times a friend or colleague would defer to one of their friends or colleagues
to answer the question. "So I thought why not build a new system to automate
this process," he said.

The system is designed to make it easier to get information that
is complicated, too new to be part of an organizational knowledge base,
or too valuable for its owners to make it public, said Zhang. The system
consists of instant messaging software and a pair of advanced functions
that support the social network-based search process, said Zhang.

First, the program maintains a much more complicated user profile
than most instant messengers, said Zhang. "Besides letting a user input
his expertise and interests manually, SWIM can automatically mine a user's
homepage and browser bookmarks to construct a keyword vector to represent
the user's information identity."

Second, the program contains a referral agent that automatically
handles the information-querying process, said Zhang.

To search for information, a user sends a question to his own
referral agent, which broadcasts the query to all of the user's buddies'
agents, said Zhang. A referral agent in the buddy's messenger searches
its information identity profile to see if that person is likely to be
able to answer the question. If not, the agent either returns empty results
or forwards the query to its buddies, depending on how the user has set
the software.

When a likely match is found, that person sees the question and
the path the query traveled, said Zhang. This friend-of-a-friend or friend-of-a-friend-of-a-friend
of the questioner "can either start chatting immediately, or discuss the
questions... later if the answering person prefers not to be disturbed
at that time," he said.

There are four challenges to be met in developing this type of
software, said Zhang: identifying people's expertise or knowledge from
their electronic presence, matching questions to the right people, motivating
people to help others, and protecting privacy and avoiding problems with
spam.

The system addresses a very timely issue: quantifying the value
of informal social knowledge, said Jon Kleinberg, an associate professor
of computer science at Cornell University.

There are two sides to this, said Kleinberg. "One is the issue
of how to find people who have the relevant information." The researchers
address this by building navigational techniques into their system, he
said. Combining searching with social networks is an interesting idea,
he added.

The more difficult issue is how to get people to participate,
Kleinberg said. Friends help friends because they have a relationship,
but once the network gets three layers out -- friends of friends of friends
-- the person asking for help is a complete stranger to the helper.

In 2003, Columbia University sociologist Duncan Watts performed
in experiment using email to test large-scale small-word social networks
and found that many people were not motivated enough to participate. "The
attrition rate tends to be high, and if the attrition rate is high at
every step, then essentially you have this exponential decay in your ability
to find faraway information," said Kleinberg. "And so you have to start
asking about incentive mechanisms."

The Michigan researchers are showing that it's possible to build
small-world navigation techniques into a working system in an interesting
way and they are aware of the incentive issues, said Kleinberg. To assess
whether the system will work, however, requires a large-scale experiment,
he said.

The researchers are still developing and testing the tool, according
to Zhang. They have a stand-alone program and implementation that works
as a plug-in for existing instant messaging clients.

The widespread use of instant messengers in daily life and work
environments provides the critical mass of people to make the scheme viable,
said Zhang. Ultimately, the software is meant to become a human equivalent
of the Google Internet search engine for answers to non-Boolean types
of questions, he said.

The system is also part of a larger research project investigating
how information flows influence productivity, said Zhang. As part of that
project, the researchers have discovered that social networks make individuals
more efficient.

The researchers analyzed a large data set that included communications
flows and surveys of people's perceptions of email and found strong statistical
correlations between social network factors and individual output, said
Zhang. "Searching information from humans directly is the most traditional
way that people seek information," he said. "We should really re-examine
the value of this method and try to use new technology to promote it."

Zhang's research colleague was Marshall Van Alstyne. The researchers
presented the work at the Association of Computing Machinery (ACM) Computer-Human
Interaction (CHI) 2004 in Vienna, Austria April 24 to 29. The research
was funded by the National Science Foundation (NSF) and Intel Corporation.